Providede additional raw sweeplidar and is not a rigid transformation with existing sweeplidar 3D points

[TWJianNuo]

Hi! I wish to compute a rigid affine transformation between additional provided raw sweeplidar and existing sweeplidar. However, not matter I use moorse-puesodo inverse to compute or use ICP algorithmn to compute, I can not acqurie a feasible affine transformation between the two.

In visualization, I find they are in correspondence but exists distorion in existing sweeplidar. May I know what additional operation you apply to transfer the raw sweeplidar to current sweeplidar points?

[dkoguciuk]

I was just going to describe the exact same problem.

I've downloaded raw data given the link here: #67
I've read the raw point cloud in lidar coordinate frame like shown here: #77

Then, I've tried to estimate rigid transformation from the world/ego frame to raw lidar frame, and this is the result:

@nisseknudsen , you know anything about it? Or maybe you could provide the transformation from ego frame to lidar frame, so we won't have to reverse engineer it?

[xpchuan-95]

I was just going to describe the exact same problem.

I've downloaded raw data given the link here: #67
I've read the raw point cloud in lidar coordinate frame like shown here: #77

Then, I've tried to estimate rigid transformation from the world/ego frame to raw lidar frame, and this is the result:

@nisseknudsen , you know anything about it? Or maybe you could provide the transformation from ego frame to lidar frame, so we won't have to reverse engineer it?

I think transform between raw pointcloud and pandaset-pointcloud is not a rigid transformation because of motion compensation used.

[dkoguciuk]

@xpchuan-95 , sure, I am ok with motion compensation :) I would be even more ok if the rigid transformation from ego frame to lidar frame would be provided by PandaSet authors :)

[jlbslbs]

I would like to have the transformation from ego frame to the sensor frames as well! @xpchuan-95